摘要:随着定位技术的高速发展，定位传感器被广泛应用于智能手机、车载导航等移动设备中，用于采集移动对象位置数据并将数据上传至服务器.该技术的应用方便了位置跟踪、预测和分析，同时也带来了轨迹数据量大、数据冗余、传输和存储代价高等问题.轨迹压缩技术即是针对该问题而提出的，它通过保留关键轨迹点和去除冗余轨迹点信息，降低了轨迹数据的传输和存储开销.分析了近年来轨迹压缩领域的研究进展，针对现有研究工作的不足，提出了一种路网感知的在线轨迹压缩方法，包括针对轨迹压缩的距离有界的隐马尔可夫地图匹配算法和误差有界的高效轨迹压缩算法等，实现了该方法的原型系统ROADER （road-network aware and error-bounded trajectory compression）.基于真实数据集的实验结果表明，该系统在压缩率、误差和执行时间等方面均显著优于同类算法.



Abstract:With the rapid development of positioning technologies, positioning sensors are widely used in smart phones, car navigation system and other mobile devices. These positioning systems collect data points at certain sampling rates and produce massive trajectories, which further bring the challenges of storage and transmission of the trajectory data. The trajectory compression technique reduces the waste of the network bandwidth and the storage space by removing the redundant trajectory points and preserving the key trajectory points. This paper summarizes the progresses of trajectory compression researches and proposes a road-network aware and error bounded online trajectory compression system, named ROADER. The system includes a distance-bounded Hidden Markov map matching algorithm and error-bounded efficient trajectory compression algorithm. Experiments based on real data sets show that the system is superior to similar systems in terms of compression ratio, error occurrence and running time.



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